Instructions to use datapaf/DeepSeekCoderCodeQnA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use datapaf/DeepSeekCoderCodeQnA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="datapaf/DeepSeekCoderCodeQnA")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("datapaf/DeepSeekCoderCodeQnA")
model = AutoModelForCausalLM.from_pretrained("datapaf/DeepSeekCoderCodeQnA")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use datapaf/DeepSeekCoderCodeQnA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "datapaf/DeepSeekCoderCodeQnA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "datapaf/DeepSeekCoderCodeQnA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/datapaf/DeepSeekCoderCodeQnA

SGLang

How to use datapaf/DeepSeekCoderCodeQnA with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "datapaf/DeepSeekCoderCodeQnA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "datapaf/DeepSeekCoderCodeQnA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "datapaf/DeepSeekCoderCodeQnA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "datapaf/DeepSeekCoderCodeQnA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use datapaf/DeepSeekCoderCodeQnA with Docker Model Runner:
```
docker model run hf.co/datapaf/DeepSeekCoderCodeQnA
```

Model Card for DeepSeekCodeCodeQ&A

This is a version of DeepSeek-Coder model that was fine-tuned on the grammatically corrected texts.

Model Details

Model Description

Model type: LLaMa
Number of Parameters: 6.7B
Supported Programming Language: Python
Finetuned from model: DeepSeek-Coder

Model Sources [optional]

Repository: GitHub Repo
Paper: "Leveraging Large Language Models in Code Question Answering: Baselines and Issues" Georgy Andryushchenko, Vladimir V. Ivanov, Vladimir Makharev, Elizaveta Tukhtina, Aidar Valeev

How to Get Started with the Model

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('deepseek-ai/deepseek-coder-6.7b-instruct')
model = AutoModelForCausalLM.from_pretrained('datapaf/DeepSeekCoderCodeQnA', device_map="cuda")

code = ... # Your Python code snippet here
question = ... # Your question regarding the snippet here

q = f"{question}\n{code}"

inputs = tokenizer.encode(q, return_tensors="pt").to('cuda')
outputs = model.generate(inputs, max_new_tokens=512, pad_token_id=tokenizer.eos_token_id)
text = tokenizer.decode(outputs[0])
print(text)

-->

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

F32

Paper for datapaf/DeepSeekCoderCodeQnA

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45